Our interest in whether information in the training data is also present in the output of the recommender is reminescent of the idea of calibration [1]}.
An uncalibrated recommender system has a mismatch between properties of the training data and of the output.
The properties conventionally studied in the literature, e.g., by [1]}, are item attributes.
Here we are looking at user attributes.
Like [1]} we find that consistency between the input and the output has an interesting impact above and beyond producing better recommendations in terms of prediction accuracy.
